Recently, manifold learning has been widely exploited in pattern recognition, data analysis, and machine learning. This paper presents a novel framework, called Riemannian manifold learning (RML), based on the assumption that the input high-dimensional ...
We develop a framework for polynomial regression on Riemannian manifolds. Unlike recently developed spline models on Riemannian manifolds, Riemannian polynomials offer the ability to model parametric polynomials of all integer orders, odd and even. An ...
In this paper, we present a geodesic discriminant analysis (GDA) algorithm, which generalize linear discriminant analysis (LDA) in linear manifold space to curved Riemannian manifold space, with the aid of Riemannian logarithmic map. Compared with LDA, ...
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